Class MISVM

  • All Implemented Interfaces:
    java.io.Serializable, java.lang.Cloneable, CapabilitiesHandler, MultiInstanceCapabilitiesHandler, OptionHandler, RevisionHandler, TechnicalInformationHandler

    public class MISVM
    extends Classifier
    implements OptionHandler, MultiInstanceCapabilitiesHandler, TechnicalInformationHandler
    Implements Stuart Andrews' mi_SVM (Maximum pattern Margin Formulation of MIL). Applying weka.classifiers.functions.SMO to solve multiple instances problem.
    The algorithm first assign the bag label to each instance in the bag as its initial class label. After that applying SMO to compute SVM solution for all instances in positive bags And then reassign the class label of each instance in the positive bag according to the SVM result Keep on iteration until labels do not change anymore.

    For more information see:

    Stuart Andrews, Ioannis Tsochantaridis, Thomas Hofmann: Support Vector Machines for Multiple-Instance Learning. In: Advances in Neural Information Processing Systems 15, 561-568, 2003.

    BibTeX:

     @inproceedings{Andrews2003,
        author = {Stuart Andrews and Ioannis Tsochantaridis and Thomas Hofmann},
        booktitle = {Advances in Neural Information Processing Systems 15},
        pages = {561-568},
        publisher = {MIT Press},
        title = {Support Vector Machines for Multiple-Instance Learning},
        year = {2003}
     }
     

    Valid options are:

     -D
      If set, classifier is run in debug mode and
      may output additional info to the console
     -C <double>
      The complexity constant C. (default 1)
     -N <default 0>
      Whether to 0=normalize/1=standardize/2=neither.
      (default: 0=normalize)
     -I <num>
      The maximum number of iterations to perform.
      (default: 500)
     -K <classname and parameters>
      The Kernel to use.
      (default: weka.classifiers.functions.supportVector.PolyKernel)
     
     Options specific to kernel weka.classifiers.functions.supportVector.PolyKernel:
     
     -D
      Enables debugging output (if available) to be printed.
      (default: off)
     -no-checks
      Turns off all checks - use with caution!
      (default: checks on)
     -C <num>
      The size of the cache (a prime number), 0 for full cache and 
      -1 to turn it off.
      (default: 250007)
     -E <num>
      The Exponent to use.
      (default: 1.0)
     -L
      Use lower-order terms.
      (default: no)
    Version:
    $Revision: 9144 $
    Author:
    Lin Dong (ld21@cs.waikato.ac.nz)
    See Also:
    SMO, Serialized Form
    • Field Detail

      • FILTER_NORMALIZE

        public static final int FILTER_NORMALIZE
        Normalize training data
        See Also:
        Constant Field Values
      • FILTER_STANDARDIZE

        public static final int FILTER_STANDARDIZE
        Standardize training data
        See Also:
        Constant Field Values
      • FILTER_NONE

        public static final int FILTER_NONE
        No normalization/standardization
        See Also:
        Constant Field Values
      • TAGS_FILTER

        public static final Tag[] TAGS_FILTER
        The filter to apply to the training data
    • Constructor Detail

      • MISVM

        public MISVM()
    • Method Detail

      • globalInfo

        public java.lang.String globalInfo()
        Returns a string describing this filter
        Returns:
        a description of the filter suitable for displaying in the explorer/experimenter gui
      • getTechnicalInformation

        public TechnicalInformation getTechnicalInformation()
        Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
        Specified by:
        getTechnicalInformation in interface TechnicalInformationHandler
        Returns:
        the technical information about this class
      • listOptions

        public java.util.Enumeration listOptions()
        Returns an enumeration describing the available options
        Specified by:
        listOptions in interface OptionHandler
        Overrides:
        listOptions in class Classifier
        Returns:
        an enumeration of all the available options
      • setOptions

        public void setOptions​(java.lang.String[] options)
                        throws java.lang.Exception
        Parses a given list of options.

        Valid options are:

         -D
          If set, classifier is run in debug mode and
          may output additional info to the console
         -C <double>
          The complexity constant C. (default 1)
         -N <default 0>
          Whether to 0=normalize/1=standardize/2=neither.
          (default: 0=normalize)
         -I <num>
          The maximum number of iterations to perform.
          (default: 500)
         -K <classname and parameters>
          The Kernel to use.
          (default: weka.classifiers.functions.supportVector.PolyKernel)
         
         Options specific to kernel weka.classifiers.functions.supportVector.PolyKernel:
         
         -D
          Enables debugging output (if available) to be printed.
          (default: off)
         -no-checks
          Turns off all checks - use with caution!
          (default: checks on)
         -C <num>
          The size of the cache (a prime number), 0 for full cache and 
          -1 to turn it off.
          (default: 250007)
         -E <num>
          The Exponent to use.
          (default: 1.0)
         -L
          Use lower-order terms.
          (default: no)
        Specified by:
        setOptions in interface OptionHandler
        Overrides:
        setOptions in class Classifier
        Parameters:
        options - the list of options as an array of strings
        Throws:
        java.lang.Exception - if an option is not supported
      • getOptions

        public java.lang.String[] getOptions()
        Gets the current settings of the classifier.
        Specified by:
        getOptions in interface OptionHandler
        Overrides:
        getOptions in class Classifier
        Returns:
        an array of strings suitable for passing to setOptions
      • kernelTipText

        public java.lang.String kernelTipText()
        Returns the tip text for this property
        Returns:
        tip text for this property suitable for displaying in the explorer/experimenter gui
      • getKernel

        public Kernel getKernel()
        Gets the kernel to use.
        Returns:
        the kernel
      • setKernel

        public void setKernel​(Kernel value)
        Sets the kernel to use.
        Parameters:
        value - the kernel
      • filterTypeTipText

        public java.lang.String filterTypeTipText()
        Returns the tip text for this property
        Returns:
        tip text for this property suitable for displaying in the explorer/experimenter gui
      • setFilterType

        public void setFilterType​(SelectedTag newType)
        Sets how the training data will be transformed. Should be one of FILTER_NORMALIZE, FILTER_STANDARDIZE, FILTER_NONE.
        Parameters:
        newType - the new filtering mode
      • getFilterType

        public SelectedTag getFilterType()
        Gets how the training data will be transformed. Will be one of FILTER_NORMALIZE, FILTER_STANDARDIZE, FILTER_NONE.
        Returns:
        the filtering mode
      • cTipText

        public java.lang.String cTipText()
        Returns the tip text for this property
        Returns:
        tip text for this property suitable for displaying in the explorer/experimenter gui
      • getC

        public double getC()
        Get the value of C.
        Returns:
        Value of C.
      • setC

        public void setC​(double v)
        Set the value of C.
        Parameters:
        v - Value to assign to C.
      • maxIterationsTipText

        public java.lang.String maxIterationsTipText()
        Returns the tip text for this property
        Returns:
        tip text for this property suitable for displaying in the explorer/experimenter gui
      • getMaxIterations

        public int getMaxIterations()
        Gets the maximum number of iterations.
        Returns:
        the maximum number of iterations.
      • setMaxIterations

        public void setMaxIterations​(int value)
        Sets the maximum number of iterations.
        Parameters:
        value - the maximum number of iterations.
      • buildClassifier

        public void buildClassifier​(Instances train)
                             throws java.lang.Exception
        Builds the classifier
        Specified by:
        buildClassifier in class Classifier
        Parameters:
        train - the training data to be used for generating the boosted classifier.
        Throws:
        java.lang.Exception - if the classifier could not be built successfully
      • distributionForInstance

        public double[] distributionForInstance​(Instance exmp)
                                         throws java.lang.Exception
        Computes the distribution for a given exemplar
        Overrides:
        distributionForInstance in class Classifier
        Parameters:
        exmp - the exemplar for which distribution is computed
        Returns:
        the distribution
        Throws:
        java.lang.Exception - if the distribution can't be computed successfully
      • main

        public static void main​(java.lang.String[] argv)
        Main method for testing this class.
        Parameters:
        argv - should contain the command line arguments to the scheme (see Evaluation)